Testing Quantum and Simulated Annealers on the Drone Delivery Packing Problem

TL;DR

This paper formulates the drone delivery packing problem as a QUBO and compares the performance of quantum annealing with classical methods, revealing their respective advantages and limitations.

Contribution

It introduces two QUBO formulations of the DDPP and evaluates quantum annealing against classical algorithms for this logistics optimization problem.

Findings

Quantum annealers show potential but have limitations compared to classical methods.

Classical algorithms outperform quantum annealers in certain scenarios.

QUBO formulations enable testing of quantum approaches on real-world problems.

Abstract

Using drones to perform human-related tasks can play a key role in various fields, such as defense, disaster response, agriculture, healthcare, and many others. The drone delivery packing problem (DDPP) arises in the context of logistics in response to an increasing demand in the delivery process along with the necessity of lowering human intervention. The DDPP is usually formulated as a combinatorial optimization problem, aiming to minimize drone usage with specific battery constraints while ensuring timely consistent deliveries with fixed locations and energy budget. In this work, we propose two alternative formulations of the DDPP as a quadratic unconstrained binary optimization (QUBO) problem, in order to test the performance of classical and quantum annealing (QA) approaches. We perform extensive experiments showing the advantages as well as the limitations of quantum annealers for this optimization problem, as compared to simulated annealing (SA) and classical state-of-the-art commercial tools for global optimization.